Abstract for syn_thesis

PhD thesis, University of Cambridge

MODEL-BASED THREE-DIMENSIONAL FREEHAND ULTRASOUND IMAGING

Michael Syn

May 1996

A 3D freehand ultrasound system augments a conventional clinical
scanner with a position sensor on the hand-held probe. Such systems
are safe, cheap, portable, and allow clinicians to scan using
conventional techniques. Unfortunately the resulting freehand images
are non-parallel, sometimes self-intersecting, and retain the noisy
image artefacts inherent in conventional 2D ultrasound.

This dissertation proposes two model-based strategies for interpreting
such images: an organ shape model is used for geometric reconstruction
of scattered organ landmarks in the images, and the Gompertz growth
model is used to register organ shape models to each other in a
coherent and biologically justified way.

Both strategies are robust to noise and inaccuracies in the organ
model meshes, and are intended to complement future work on the
detection of tissue boundaries in ultrasound images. So a model-based
framework to organise sparse and noisy cues about tissue boundaries,
is a key element in any attempt at fully-automated interpretation of
3D freehand ultrasound images.

A biological model of organ growth is first developed using
Oster-Murray mechanisms, whose eigenmodes describe the organ's modes
of shape variation. An iterative procedure allows these idealised
modes to be refined from organ examples. 3D freehand ultrasound
images are then segmented by such organ models, for the purpose of
organ volume estimation. However, an organ model can only be refined
from the segmented organ shape if they both share a common shape
parameterisation.

They are therefore registered to each other using their eigenmodes,
which are proposed to represent homologous (`biologically
corresponding') landmarks. The choice of registration solutions is
restricted to biologically plausible ones using the Gompertz metric.
Bayesian combination of the likelihood of eigenmode homology, with the
prior constraint of Gompertzian growth, results in a posterior measure
of homology which must be minimised for an optimal registration. The
minimisation is efficiently performed using a multi-resolution
implementation of the highest confidence first algorithm.

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